Health & Life Sciences
Biotechnology
In biotechnology, data is both high-dimensional and expensive, which makes methods that reduce the number of experiments valuable.
Problems we address in this sector
- Analysis of high-dimensional measurement data and pattern extraction
- Prioritising experimental design by expected information gain
- Structuring laboratory records and making them searchable
- Supporting biological processes with simulation
Approaches we apply
- Generative AI & LLMsRAG architectures, domain adaptation and fine-tuning, distillation into smaller models, Turkish evaluation sets and on-premise deployment.
- MLOps & Model OperationsEvaluation infrastructure, observability, hallucination and regression tracking, continuous retraining.
- Reinforcement Learning & Decision SystemsDecision policies that adapt to changing conditions, reward design, training in simulation and safe transfer to the field.
Other sectors in this group
Do you have a problem in this sector?
Let's define the problem together in a technical consultation; feasibility is assessed at the first stage.